MAINT: More logistic regression deselections due to numerical issues #2798
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ethanglaser,
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| - tests/test_common.py::test_estimators[StackingClassifier(estimators=[('est1',LogisticRegression(C=0.1)),('est2',LogisticRegression(C=1))])-check_sample_weights_invariance(kind=zeros)] <1.1 | ||
| # Here it doesn't reach convergence under the default maximum iterations, and the test | ||
| # turns the convergence warning into an error. Note that it has numeric differences w.r.t. | ||
| # the original sklearn, so it's not guaranteed to reach the same optimality after each iteration. |
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Is it possible to suppress warnings for this particular test instead of removing it? What are the further steps? Do we want to leave it as deselected forever or some modification to logistic regression is planned?
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The test is explicitly checking that no warning is issued.
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/azp run Nightly |
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Azure Pipelines successfully started running 1 pipeline(s). |
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Description
Another deselection for a scikit-learn test about logistic regression which appears to be failing due to numerical tolerance issues.
Checklist:
Completeness and readability
Testing